在Python中使用MNIST方式准备/渲染用于图像分类的图像

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# 将图像数据转换为浮点数类型，并归一化到0-1之间
train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255

# 将图像数据转换为二维数组
train_images = train_images.reshape((60000, 28 * 28))
test_images = test_images.reshape((10000, 28 * 28))

# 选择一个图像进行渲染
image_index = 0
plt.imshow(train_images[image_index].reshape(28, 28), cmap='gray')
plt.title('Label: ' + str(train_labels[image_index]))
plt.show()